Paper Citation Record · LEDGER

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification

As of 9 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2608.04899.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.04899 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:05:41.210502Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

45 of 45 outbound references displayed

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unresolved33
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External citation measurements

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Outbound references

Observation 67c64fc7-7a01-438b-9011-c290c8f887c0 · outbound

This paper cites Benchmarking Uncertainty Quantification Methods for Large Language Models with LM -Polygraph.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Benchmarking Uncertainty Quantification Methods for Large Language Models with LM -Polygraph

Reference 1

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Observation 7a7e6c93-a5b9-478e-b940-8ca88100279b · outbound

This paper cites Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Beyond Semantic Entropy: Boosting LLM Uncertainty Quantification with Pairwise Semantic Similarity

Reference 2

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Thinking Out Loud: Do Reasoning Models Know When They ' re Right?

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This paper cites Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification C heck E val: A reliable LLM -as-a-Judge framework for evaluating text generation using checklists

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Unresolved cited work

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Observation 8424d035-3c56-4419-b41d-6805fe56d743 · outbound

This paper cites Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Unconditional Truthfulness: Learning Unconditional Uncertainty of Large Language Models

Reference 7

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This paper cites Uncertainty Quantification for In-Context Learning of Large Language Models.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Uncertainty Quantification for In-Context Learning of Large Language Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification A Survey of Confidence Estimation and Calibration in Large Language Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification B ayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models

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Observation 84f94c29-51eb-4379-8f71-c1d0d6811834 · outbound

This paper cites Calibrating the Confidence of Large Language Models by Eliciting Fidelity.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Calibrating the Confidence of Large Language Models by Eliciting Fidelity

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Observation 09f6206a-bb22-4359-93d7-67e3bde20ca6 · outbound

This paper cites Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Contextualized Sequence Likelihood: Enhanced Confidence Scores for Natural Language Generation

Reference 12

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This paper cites Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Shifting Attention to Relevance: Towards the Predictive Uncertainty Quantification of Free-Form Large Language Models

Reference 13

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This paper cites Adaptation with Self-Evaluation to Improve Selective Prediction in LLM s.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Adaptation with Self-Evaluation to Improve Selective Prediction in LLM s

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This paper cites Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Just Ask for Calibration: Strategies for Eliciting Calibrated Confidence Scores from Language Models Fine-Tuned with Human Feedback

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification and Ng, Andrew and Potts, Christopher

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Unresolved cited work

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This paper cites Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =.

Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Proceedings of the 36th International Conference on Neural Information Processing Systems , articleno =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification 2025 , eprint=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Transactions on Machine Learning Research , issn=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Calibrating Verbalized Probabilities for Large Language Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification IEEE Transactions on Software Engineering , year =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification The Eleventh International Conference on Learning Representations (ICLR 2023) , year=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification 2022 , eprint=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Qwen3 Technical Report

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Journal of Machine Learning Research , year =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Proceedings of the 38th International Conference on Neural Information Processing Systems , articleno =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification BingoGuard:

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification SMARTER: A Data-efficient Framework to Improve Toxicity Detection with Explanation via Self-augmenting Large Language Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification arXiv e-prints , pages=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Journal of classification , volume=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Genome Biology , volume=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Proceedings of the 33rd ACM International Conference on the Foundations of Software Engineering , pages =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification and Zhang, Hao and Gonzalez, Joseph E

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Proceedings of the third International Workshop on Machine Learning in Systems Biology , pages =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining , year =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification I Can't Believe It's Not Better: Failure Modes in the Age of Foundation Models

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification arXiv preprint arXiv:2506.01734 , year=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Advances in Neural Information Processing Systems (NeurIPS 2015) , year =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification , author=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification arXiv preprint arXiv:2509.13813 , year=

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification M onte C arlo Temperature: a robust sampling strategy for LLM ' s uncertainty quantification methods

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Scikit-learn: Machine Learning in Python , year =

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Evaluation Pitfalls and Sparsity Limitations in LLM-based Confidence Estimates for Classification Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models

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Pith citing papers

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